model fixed rename

README.md

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 ---
+language: en
+tags:
+- exbert
+
 license: mit
 ---
+
+
+# GPT-2
+
+Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
+
+Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
+[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
+and first released at [this page](https://openai.com/blog/better-language-models/).
+
+Disclaimer: The team releasing GPT-2 also wrote a
+[model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card
+has been written by the Hugging Face team to complete the information they provided and give specific examples of bias.
+
+## Model description
+
+GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This
+means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots
+of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
+it was trained to guess the next word in sentences.
+
+More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
+shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
+predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
+
+This way, the model learns an inner representation of the English language that can then be used to extract features
+useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
+prompt.
+
+## Intended uses & limitations
+
+You can use the raw model for text generation or fine-tune it to a downstream task. See the
+[model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you.
+
+### How to use
+
+Here is how to use the ONNX models of gpt2 to get the features of a given text:
+
+Example using transformers.pipelines:
+
+```python
+from transformers import AutoTokenizer, pipeline
+from optimum.onnxruntime import ORTModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("gpt2")
+model = ORTModelForCausalLM.from_pretrained("gpt2", from_transformers=True)
+onnx_gen = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+text = "My name is Philipp and I live in Germany."
+gen = onnx_gen(text)
+```
+
+Example of text generation:
+
+```python
+from transformers import AutoTokenizer
+from optimum.onnxruntime import ORTModelForCausalLM
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("optimum/gpt2")
+model = ORTModelForCausalLM.from_pretrained("optimum/gpt2")
+
+inputs = tokenizer("My name is Arthur and I live in", return_tensors="pt")
+
+gen_tokens = model.generate(**inputs,do_sample=True,temperature=0.9, min_length=20,max_length=20)
+tokenizer.batch_decode(gen_tokens)
+```

config.json

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+{
+  "_name_or_path": "gpt2",
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 50256,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 50256,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 12,
+  "n_positions": 1024,
+  "pad_token_id": 0,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50
+    }
+  },
+  "transformers_version": "4.24.0",
+  "use_cache": true,
+  "vocab_size": 50257
+}

model.onnx

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